Week 6 - Data analysis

AIM : To write a script that does data analysis on python.

OBJECTIVE :

1. Visualising the data, i.e., to plot graph between any two quantities according to user's input.
2. Compatibility Check, i.e., code should exit gracefully, if a non-compatible file is provided as an input.
3. To calculate basic engine performance calculations.

INTRODUCTION :

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data analysis is a somewhat abstract concept to understand without the help of examples. So to better illustrate how and why data analysis is important for businesses, here are the 4 types of data analysis and examples of each.

• Descriptive Analysis: Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more.
• Diagnostic Analysis: Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.
• Predictive Analysis: Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year’s revenue, data from previous years will be analyzed. If revenue has gone up 20% every year for many years, we would predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads.
• Prescriptive Analysis: Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data-driven choices are made.

These 4 types of data analysis can be applied to any issue with data related to it. And with the internet, data can be found about pretty much everything.

GOVERNING EQUATIONS USED :
Since the data file given consists of various parameters for an engine. Several calculations can be made from it. But for our interest the following parameters are to be calculated-

• Work done by the engine (`W_(out)): This can be found by plotting p-V diagram of the engine and calculating area under the curve.
• Power output of the engine : This is generally called engine brake power and can be found as,

`Power = (W_(out)*RPM*n)/(60*1000*2)` in `kW` . Here `n` is the number of cylinders of the engine , `RPM` is the engine speed in minutes, it's to be converted into seconds by dividing by `60`, `2` in denominator term is present because the given engine is a 4 stroke engine.

• Specific fuel consumption (`SFC`) : `(FC)/(Power)` , where `FC` is the amount of fuel consumed by the engine in `(kg)/(hr)` .

INPUTS : `RPM` = 1500 , fuel consumed per stroke = `20` micro grams.

PROCEDURE :

• At first important modules such as math, matplotlib, numpy, scipy are imported for their various functions.

• For checking compatibility of the program, 'try' and 'except' command is used according to their syntax and the given data file is opened using 'open' command within a for loop. If the file name is misspelled or is wrong, the code will yield error with the following message "File not recognized. Please provide a valid CONVERGE output file" and will not execute and exit.

• For the purpose of plotting curve between two quantities according to user input, first a line counter is initiated from 1 and null arrays of volume and pressure and data is created for further uses. Now an 'if' loop is iterated with the condition that if '#' is not in line, (i.e., if only numerical digits is present and not any other line) all the numerical values present in the data file is now to be stored in the data array as string (by default).

• Now another 'if' loop is executed within with the condition that if line counter is greater than or equal to 6 all the data will be stored in pressure and volume array repectively.
• For plotting p-V diagram the above pressure and volume array are plotted accordingly.

• For engine performance calculations, first area under pV curve is to be calculated and this can be done by either Trapezoidal method or Simpson method. Here it has been calculated by both methods by entering the required inputs and the necesary formulas.

• The array of data obtained earlier consists of column matrix of strings of each quantites present inside it. So the column matrix is transposed into several row matrices according to various quantities using 'np.transpose' command and is further converted into float data types using 'astype' command.

• Two arrays of name of the parameters and their units are made according to data file given and then user has to choose from them to plot respective graphs. For this the two inputs of user is taken using 'input' command in python 3 and its datatype is set as 'int' and plotted using 'plt.plot' command. Basically user is entering column number as input and accordingly result is generated.

PYTHON CODE :

# A program to calculate engine parameters and to plot graph between two quantites according to user input
import math
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import simps

# Compatability check
try:
  open('engine_data.out')

  line_count = 1

  # Creating of empty arrays to store the value of Volume and pressure respectively
  Volume = []
  pressure = []

  # Volume and pressure in the data file is present in column 8 and 2 respectively. Calling them and storing their data into arrays 
  v = 8
  p = 2

  # Creating a null array and storing all the values of data file into it

  data=[]

  for line in open('engine_data.out'):
        if '#' not in line:
          data.append(line.split())
          if line_count >= 6:
                Volume.append(float(line.split()[v-1]))
                pressure.append(float(line.split()[p-1]))
        line_count = line_count + 1


  # Plotting p-V diagram
  plt.plot(Volume, pressure, color = 'r', linewidth = 2)
  plt.xlabel('Volume (m^3)')
  plt.ylabel('Pressure (MPa)')
  plt.title("p-V diagram of Engine")
  plt.grid()
  plt.savefig('p-V diagram of Engine')
  plt.show()

    
  # Calculation of area under PV diagram using the simpsons rule
    
  area = simps(pressure, Volume)
  print('Area under the curve using Simpsons Rule : ' + str(area) + ' units')
  print('Work done by the engine : ' + str(area) + ' MJ')
  area = area*1e6
  print('Work done by the engine : ' + str(area) + ' J')
    
  #Inputs
  #Speed of an engine (RPM)
  rpm = 1500
  rps = 1500/60
  stroke_cycle = rps/2

  #Fuel consumption in micro grams per 1 stroke cycle
  fuel_consumption = 20

  #Engine Specifications
  #Number of cylinder=
  n = 1
  # 4 stroke engine
    
  # Calcualtions of Power output and specific fuel consumption
    
  power_output = n*area*stroke_cycle/(1000)
  print('Power ouput from engine : ' + str(power_output) + ' KW')  
  SFC = (fuel_consumption*pow(10,-6)*stroke_cycle*3600)/(power_output*1000)
  print('Specific Fuel Consumption, SFC = ' + str(SFC) + ' (kg/hr)/KW')
  print('t')

  # Calculation of area under PV diagram using the trapezoidal rule and work done,power sfc
  Area = np.trapz(pressure,Volume)
  print('Area under the curve using trapezoidal Rule : ' + str(Area) + ' units')
  print('Work done by the engine : ' + str(Area) + ' MJ')
  Area = Area*1e6
  print('Work done by the engine : ' + str(Area) + ' J')
  power_output = n*Area*stroke_cycle/(1000)
  print('Power ouput from engine : ' + str(power_output) + ' KW')  
  SFC = (fuel_consumption*pow(10,-6)*stroke_cycle*3600)/(power_output*1000)
  print('Specific Fuel Consumption, SFC = ' + str(SFC) + ' (kg/hr)/KW')
  print('t')

          
  # Tansposing columns into row matrix and converting them into float type from string type
  transpose=np.transpose(data)
  float_f=transpose.astype(np.float)

   # Array for header and their units
  parameters = ['Crank','Pressure','Maximum Pressure','Minimum Pressure','Mean Temperature','Maximum Temperature','Minimum Temperature','Volume','Mass','Density','Integrated HR','HR Rate','Cp','Cv','Gamma','Kinematic Viscosity','Dynamic Viscosity']

  units = ['(DEG)','(MPa)','(MPa)','(MPa)','(K)','(K)','(K)','(m^3)','(kg)','(kg/m^3)','(J)','(J/time)','(J/kgK)','(J/kgK)','','(m^2/s)','(Ns/m^2)']

  # Providing user choice of picking two parameters for plotting
  print('Choose from the following options and press enter key')
  print(' 1 Crank n 2 Pressure n 3 Maximum Pressure n 4 Minimum Pressure n 5 Mean Temperature n 6 Maximum Temperature n 7 Minimum Temperature n 8 Volume n 9 Mass n 10 Density n 11 Integrated HR n 12 HR Rate n 13 Cp n 14 Cv n 15 gamma n 16 Kinematic Viscosity n 17 Dynamic Viscosity')

  X=int(input('Enter your choice on X-axis n'))

  Y=int(input('Enter your choice on Y-axis n'))

  # Plotting the graph based on user choice
  plt.figure(1)
  plt.plot(float_f[X-1],float_f[Y-1],linewidth=3,color='blue')
  plt.title(' Plot for ' + parameters[X-1]+ ' versus ' +parameters[Y-1],color='blue')
  plt.xlabel(parameters[X-1] + ' ' + units[X-1])
  plt.ylabel(parameters[Y-1] + ' ' + units[Y-1],)
  plt.savefig(parameters[X-1] + ' vs ' + parameters[Y-1] + '.png')
  plt.grid()
  plt.show()

except:
  print('File not recognized. Please provide a valid CONVERGE output file')

RESULTS :

• Upon executing the following program, first a p-v plot appears and according to it the required calculations are made as,

• This was made possible only if correct data file is opened. If there were different files to be opened the following message is dispalyed in the command window,

• The following plots were observed for the above input,

• Similarly other plots were made by providing input to the code and were saved in the folder by their respective name,

• Screenshot of the folder where these plots are saved automatically,

ERRORS : Several types of indentation errors were made and rectified but one error was due to not transposing the data as explained in the procedure. On running the program just yields this error. This was due to converting all the strings into float type without indicating a particular item of line.split()[]. To avoid this error, the data was appended without converting into float at line 16, but rather creating a transpose and converting into float type using 'astype' command in further steps.

Conclusion:

In the above program we basically write a script to analysis the performance of engine and plot graps between different properties to get our desired output from the data file using read function.